Abstract

To quantify the concept of similarity between classes of images
three measures and algorithms of calculation are proposed. The first measure is calculated through
the frequency of misclassification of subimages sampled randomly from images. The
second one is calculated through the cross membership of the mass center of a class in a
feature space. The third measure is defined through the membership of subimages,
using the distance between each subimage and the mass center of a class in a feature
space. We study these measures, classifying images in the coordinated clusters representation (CCR) feature space with the
minimum distance classifier. A database of images of Rosa Porriño granite tiles,
previously classified by three human experts, is used in the experiments. The calculated
similarity between classes is in excellent accordance with the qualitative evaluation by the
human experts.

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.